---
title: "Slides: Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai"
category: "slides"
video_id: "xnXqpUW_Kp8"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai

## Source Video
[Building a Smarter AI Agent with Neural RAG - Will Bryk, Exa.ai](https://www.youtube.com/watch?v=xnXqpUW_Kp8)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/xnXqpUW_Kp8/slide-001.jpg]]

OCR text:

> INNOVATIONPARTNER
> aws
> PLATINUMSPONSORS
> Graphite
> WWindsurf
> MongoDB
> daily
> augment code
> Workos

![[assets/slides/xnXqpUW_Kp8/slide-002.jpg]]

OCR text:

> E Personal
> QQ Searcr
> OB Craig . .
> Biden One API to get any information from the web
> Si Bewei nae
> Lb “Eee, ‘search ‘crawling Janewor ‘roeoarch
> e anew ee
> Res Vein tes a eee fe PES APE eR ee
> a ee A RE COOK
> SB toarpes Lorary \ geese
> E ox . . .
> Dens "
> t

![[assets/slides/xnXqpUW_Kp8/slide-003.jpg]]

OCR text:

> AIE
> Microsoft
> smol ai

![[assets/slides/xnXqpUW_Kp8/slide-004.jpg]]

OCR text:

> AIE
> Microsoft
> smol ai

![[assets/slides/xnXqpUW_Kp8/slide-005.jpg]]

OCR text:

> AIE
> Microsoft
> smol ai

![[assets/slides/xnXqpUW_Kp8/slide-006.jpg]]

OCR text:

> Traditional search engines were built
> for humans
> Humans want to type simple
> keywords to click a couple links
> Google built a keyword based
> algorithm for humans
> Humans searching
> Google

![[assets/slides/xnXqpUW_Kp8/slide-007.jpg]]

OCR text:

> The space of possible queries
> Keyword
> “stripe pricing” Pure LLM
> oN “Explain this
> ©} _— concept to me”
> 3 Semantic
> “People in SF
> who know
> assembly” —
> super-complex
> “Find me every
> “————._ article that
> ; argues X and not
> Queries no one Y from an author
> ba oughtto _ —~ like 2”
> a 7
> a a Microsoft §omol®
> _ —_~ ae :

![[assets/slides/xnXqpUW_Kp8/slide-008.jpg]]

OCR text:

> EXPLORER
> agent.py
> github_agent.pyx
> aiengineer
> defget_people():
> oerv
> ogent.py
> text-True,
> github_agent.py
> types"keyword",
> report.md
> num_resultsa1,
> urlr.resutts[e].url if r.results else
> out.append（（nane,url))
> AIE
> return out
> defagent_mark（itens);
> streanopenA.chat.completions.create(
> nodel-"gpt-40-min1",
> ]asabessau
> streanaTrue,
> S6
> encodin
> PROBLEMS
> OUTPUT
> DEBUG CONSOLE
> TERMINAL
> COMMENTS
> [Tsubasa Kato]（https://github.com/stingraze)
> zh
> [RobertoBayardol(https://github.com/roberto-bayardo)
> [EdgarMe1j]（https://github.com/ejne1j)
> Python
> [MichaelBendersky](https://bendersky-github.1o/)
> [SarthChakravarty](https://github.com/sarthak-chakraborty)
> [DanielTunkeLang](https://github.con/gsingers/search_fundamentals_course)
> [DanlelCanpos]（https://github.com/danlel-e-campos)
> [SeuparnaPalchovdhury](https://github.con/seuparma)
> [Abhay Kashyap](https1//github.com/hayabhay/)
> [Sebastian Bruch](https://github.com/TusKNNy/seismic)
> [AuSH QuUATI]（https1//github.com/ankushagarva/ankush.cc)
> [AnrAadallah]（https1//github.com/awadalLah)
> [Ruey-Cheng Chen](https1//github.com/rueycheng)
> [Antonio MalLsa]（https://github.com/amal（La）
> Shashank
> Ranaprased](https://github.con/shas
> (scBiue
> res
> NG
> villbrykgwills-MacBook-Pro-2alengineer
> oo0ciadetau
> Ln26,Col15Spaces:4UTF-8LF
> ()Python
> Microsoft
> smol

![[assets/slides/xnXqpUW_Kp8/slide-009.jpg]]

OCR text:

> OS eprmm ommaner Btw tate re et eer
> . FAL he dears oe ene ieret ieee Tg Cee NEI ENT A ttere ee SUee T font 3 @
> BL ree od
> Search
> =.
> Owens
> Ca ne Deeg tee aren AL ‘
> os = Meee ee ae Smee eyed
> ae 4
> oe feet depes .
> Sq Set oe Nt tree Ae
> gyeae aeeweg Sateen
> ‘ mt . Ptew sel Ave tee “
> oon Cree One ergo i
> tenes tee seca tee
> fewer
> Fe van taet
> Crewing
> om Meer Folens Nowe
> em ©
> Vareren rare rere ten toot inst
> Ce
> rosa I cans
> a Microsoft § Gmc
> Ps ri
> ' i a .

![[assets/slides/xnXqpUW_Kp8/slide-010.jpg]]

OCR text:

> EXPLORER
> agent.py
> Aduerqra
> angineer）agent.py>search_web
> aengineer
> oerv
> agent.py
> github_agent.py
> repor.md
> AIE
> PROBLEMS
> OUTPUT
> DEBUG CONSOLE
> TERMINAL
> COMMENTS
> Keyboardtaterrupt
> zh
> willbrykrills-MacBook-Pro-2alengineer
> Python
> willbrykglLs-MacBook-Pro-2alengineer
> willbrykgwlLs-MacBook-Pro-2alengineer
> Running agent
> Personal Mebsites of Engineers in San francisco Interested in Information Retrleval
> Searching...
> Jonathan Koren
> -Ena1tf1rstnanegjonathankoren.coe
> -**ebsite**:[jonathankoren.com]（https1//jonathankoren.com/)
> fes
> NG
> :fqdebog.
> 000cradeta
> Ln15,Col 21Spaces4UTF-8LF
> ()Python3.13.0
> Microsoft
> smol?

![[assets/slides/xnXqpUW_Kp8/slide-011.jpg]]

OCR text:

> EXPLORER
> agent.py
> github_agent.py x
> UNTm
> s
> aengineer
> exa=Exa（api_keywos.getenvEXA_API_KEY))
> oerv
> opena1-OpenAI(ap1_keyos.getenv("OPENAI_API_KEY"))
> agent.py
> deo
> print(*GETTINGPROFILE NAMES")
> res-exa.search_and_contents(
> texteTrue,
> AIE
> type-"neural",
> num_resultse10,
> names1
> for 1,hit in enumerate(res.results):
> print（fEXTRACTING NAES（1))
> name=openai.chat.completions.create(
> model=gpt-40°,
> messogesa
> ("role":"user",
> fro the text.Only output the
> "content:hit.title+"\n"+hit.text[:2e]),
> othtng else.
> PROBLEMS
> OUTPUT
> DEBUG CONSOLE
> TERMINAL
> COMMENTS
> -tred-cli*: CLI utility for secure data storage.
> zh
> Exa-AI SearchEngine
> Python
> Funding*1Raised s17 mllion in Serles A to develop anAI-centric search engine.
> on precision and better information retrieval.
> s
> -Will Bryk discusses the future of search ina sesslon highlighting technologicalinnovati
> /atche9509qzbrQqu).
> do
> Key Takeays
> -The shift froe traditional searchengines toAI-driven solutlons is seen as essential for
> -Developeenton Gitubincludes coetributfons fron Bryk focused on supporting Cryptography
> fes
> NG
> wiltbrykwills-MacBook-Pro-2alengineer
> @o00cradeta
> Ln26,Col15Spaces4UTF-8LF(）Python
> Microsoft
> smol

![[assets/slides/xnXqpUW_Kp8/slide-012.jpg]]

OCR text:

> EXPLORER
> agent.py
> github_agent.py x
> aengineer
> oerv
> model-"gpt-40",
> agent.py
> nessages=
> dubeqrnp
> {"role”:"user,"content"：hit.title+"\n”•hit.text[:2ee]),
> othing else
> ).choices[e].message.content.strip()
> Ifnane:
> AIE
> (aueu)puadde·soueu
> print("nases",nases)
> return List(dict.fromkeys(nanes))
> defget_people():
> out=[1
> for name in get_nanes():
> print（"GETTDNGGITHUB INFO")
> r-exa.search_and_contents(
> textaTrue.
> PROBLEMS
> OUTPUT
> DEBUG CONSOLE
> TERMINAL
> COMVENTS
> zs
> names['Jonathan Koren',None','Doug TurmbuLl',“None',
> 'None','None','Aditya Varun Chadha',
> Python
> GETTDNG GITHUB INFO
> GETTINGGITHUB INFO
> GETTDNG GITHUB INFO
> GETTING GITHUB INFO
> [3onathan Koren]（https://github.con/}dkoren)
> [DougTurnbulL]（https1//github.co/softaredoug)
> None]（https1//github.com/none-None1)
> Areyouusing a screen reader to operateVS Code?
> [Aditya Varun Chadha](https1//github.com/adichad)
> [RobertoBayardo](https://github.com/rob
> [Tsubasa Kato](https://github.com/stingraze)
> res
> NG
> wilLbrykgwilLs-MacBook-Pro-2alengineer
> o00cimadea
> Ln26,Col15Spaces:4UTF-8LF
> (）Python
> Microsoft
> smol

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.